Remote data monitoring and collection system with multi-tiered analysis

ABSTRACT

A system collects and stores data from a source at a high resolution and/or a high data rate (“more detailed data”) and sends a low-resolution and/or downsampled version of the data (“less detailed data”) to a remote server via a wireless network. The server automatically analyzes the less detailed data to detect an anomaly, such as an arrhythmia, earthquake or failure of a structural member. A two-tiered analysis scheme is used, where the first tier is less specific than the second tier. If the first tier analysis detects or suspects the anomaly, the server signals the data collector to send more detailed data that corresponds to a time period associated with the anomaly. The more specific second tier analyses the more detailed data to verify the anomaly. The server may also store the received data and make it available to a user, such as via a graphical or tabular display.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 61/476,072, filed Apr. 15, 2011, titled “Remote HealthMonitoring System,” the entire contents of which are hereby incorporatedby reference herein, for all purposes.

TECHNICAL FIELD

The present invention relates to remote data monitoring and datacollecting systems and, more particularly, to remotely-controlled datamonitoring and collecting systems that employ multi-tiered analysis.

BACKGROUND ART

Remote data monitoring for anomalous behavior, such as remote monitoringof seismic sensors, volcanic sensors, oil or gas wells, weatherstations, ocean buoys and the like, poses data communication challenges,particularly when the monitored item is located far fromtelecommunications infrastructure or regular sources of electric power.For example, remote monitoring of ambulatory patients enables doctors todetect or diagnose heart problems, such as arrhythmias, that may produceonly transient symptoms and, therefore, may not be evident when thepatients visit the doctors' offices. Several forms of cardiac eventmonitors have been used.

A “Holter” monitor is worn by a patient and collects and stores data fora period of time, typically at least 24 hours, and in some cases up totwo weeks. After the data has been collected, the Holter monitor istypically brought or sent to a doctor's office, laboratory or the like,and the data is retrieved from the monitor and analyzed. Holter monitorsare relatively inexpensive, but they cannot be used for real-timeanalysis of patient data, because the data is analyzed hours, days orweeks after it has been collected.

More timely analysis of heart data is made possible by pre-symptom(looping memory) event monitors. Such a device collects and storespatient data in a “loop” memory device. The event monitor constantlyoverwrites previously stored data with newly collected data. The eventmonitor may include a button, which the patient is instructed to actuateif the patient feels ill or otherwise detects a heart-related anomaly.In response, the event monitor continues to record data for a shortperiod of time and then stops recording, thereby retaining data for atime period that spans the button actuation, i.e., the retained datarepresents a period of time that extends from (typically) a few minutesbefore the user actuated the button to (typically) a few minutes afterthe user actuated the button. The retained data may then be sent via amodem and a telephone connection to a doctor's office or to a laboratoryfor analysis. Although such an event monitor can facilitate analysis ofpatient data more proximate in time to the patient-detected anomaly,relying on the patient to actuate the device and then send the data canbe problematic.

Some event monitors automatically detect certain arrhythmias and, inresponse, record electrocardiograph (ECG) data. Automatic event monitorsare thought to be more sensitive, but less specific, than manuallytriggered cardiac event monitors for significant cardiac arrhythmias.However, these devices still rely on patients to send the recorded datafor analysis, and there is still a delay between detection of asuspected arrhythmia and transmission of the data.

Mobile cardiovascular telemetry (MCT) refers to a technique thatinvolves noninvasive ambulatory cardiac event monitors that are capableof continuous measurements of heart rate and rhythm over several days.For example, CardioNet, Philadelphia, PA, provides an MCT device underthe trade name “Mobile Cardiac Outpatient Telemetry” (MCOT). The MCOTdevice includes an automatic ECG arrhythmia detector. The MCOT devicecouples to a cellular telephone device to immediately transmitautomatically detected abnormal ECG waveforms to a remote monitoringcenter, which can then alert a physician. The MCOT device also includesa memory capable of storing up to 96 hours of ECG waveform data, whichcan be transmitted over standard telephone lines to the remotemonitoring center at the end of each day. Although data aboutautomatically detected arrhythmias are sent immediately to the remotemonitoring center, without requiring patient action, the computationalresources and corresponding electrical power (battery) required toperform the automatic ECG analysis in the MCOT device are significant.

Some MCT devices continuously send all collected ECG data to a remotemonitoring center for analysis. These MCT devices typically do notperform any ECG analysis of their own. Although no patient-initiatedaction is required, the large amount of data transmitted by the MCTwireless devices congests the wireless channels used to convey the data.Furthermore, a large amount of computational resources is required atthe remote monitoring center to analyze the continuous stream ofreceived data, especially when many patients are monitored by a singledata center.

U.S. Pat. Publ. No. 2010/0298664 discloses a wireless ECG datacollection and analysis system.

U.S. Pat. No. 7,996,187 discloses a personal health monitor thatcollects and processes physiologic data and wirelessly transmits theprocessed data to a remote entity.

U.S. Pat. Publ. No. 2009/0076405 discloses a wireless respirationmonitoring system. Upon receipt of a notification, a medical provider, aremote monitoring system or a medical treatment device can trigger ahigher data sample rate in the patient-worn monitor device and use thehigher sample rate data collected thereafter to verify an alertcondition.

U.S. Pat. No. 7,801,591 discloses a healthcare information managementsystem that displays patient information at various levels of analysis,based on user need and sophistication level.

SUMMARY OF EMBODIMENTS

An embodiment of the present invention provides a multi-tiered datacollection system for use with a remote server. The system includes adigital data input source and a transceiver assembly. The transceiverassembly includes a memory, a controller and a wireless transceiver. Thetransceiver assembly is communicatively coupled to the digital datainput source. The transceiver assembly is configured to receive datafrom the digital data input source. The transceiver assembly is alsoconfigured to store the received data in the memory. The stored data isreferred to as “more detailed data.” The transceiver assembly isconfigured to send a subset of the received data (referred to as “lessdetailed data”), via the wireless transceiver, to the remote server. Theless detailed data sent to the remote server is characterized by: alower resolution than the more detailed data stored in the memory for acorresponding time period and/or a lower sampling rate than the moredetailed data stored in the memory for a corresponding time periodand/or having been received from a different set of the sensors than themore detailed data stored in the memory for a corresponding time period.The transceiver assembly is configured to fetch at least a portion ofthe more detailed data from the memory, in response to a signal from theremote server. In addition, in response to the signal from the remoteserver, the transceiver assembly is configured to send the fetched moredetailed data to the remote server.

The less detailed data sent to the remote server may be characterized bya lower resolution than the more detailed data stored in the memory fora corresponding time period and/or a lower sampling rate than the moredetailed data stored in the memory for a corresponding time period.

The remote server may be configured to receive the less detailed datasent by the transceiver assembly and automatically analyze the receivedless detailed data for an indication of an anomaly. If the anomaly isindicated, the remote server may be configured to automatically send thesignal to the transceiver assembly.

The anomaly may be or include an earthquake; a tsunami; an unsafecondition within a gas well, an oil well or a mine; severe weather; anunsafe mechanical condition in a structural member of a construct;failure of a structural member of a construct; an unsafe conditionwithin a geological structure; a nuclear radiation level that exceeds apredetermined value; or an explosion; decompression.

The remote server may also be configured to receive the more detaileddata and automatically analyze the received more detailed data to verifythe indicated anomaly.

The remote server may be configured to analyze the less detailed dataaccording to a first analytic technique and analyze the more detaileddata according to a second analytic technique. The second analytictechnique may have a higher specificity for the anomaly than the firstanalytic technique.

The remote server may be configured to display a first user interfaceconfigured to accept at least one user-specified criterion. The remoteserver may be configured to automatically analyze the received lessdetailed data for the indication of the anomaly, based on at least aportion of the less detailed data meeting the user-specified criterion.

The remote server may be configured to display a first user interfaceconfigured to accept at least one user-specified criterion andautomatically analyze the received more detailed data to verify theindicated anomaly, based on at least a portion of the more detailed datameeting the user-specified criterion.

The wireless transceiver may include a cellular telephone.

The wireless transceiver assembly may include a cellular telephonecoupled via a short-range wireless link to the wireless transceiver. Thecellular telephone may be configured to: store the more detailed data inthe memory; send the less detailed data to the remote server; responsiveto the signal, fetch the at least the portion of the more detailed datafrom the memory and send the fetched more detailed data to the remoteserver via a wireless carrier network.

The system may also include a cellular telephone configured to becommunicatively coupled to a wireless carrier network. The cellulartelephone may be configured to receive the data sent by the transceiverassembly via the wireless transceiver and send the received data via thewireless carrier network to the remote server.

The system may also include an application program configured to beexecuted by a cellular telephone that is configured to becommunicatively coupled to a wireless carrier network. The applicationprogram may be configured to receive the data sent by the transceiverassembly via the wireless transceiver and send the received data via thewireless carrier network to the remote server.

The remote server may be configured to accept, through a first userinterface, a user-specified data collection parameter. In response toaccepting the user-specified data collection parameter, the remoteserver may be configured to send the data collection parameter to thetransceiver assembly. The transceiver assembly may be configured toreceive the data collection parameter and, in response to receipt of thedata collection parameter, to change the resolution and/or the samplingrate of the less detailed data thereafter sent to the remote server.

The remote server may be configured to generate a fist display, in afirst user interface, from the less detailed data received from thetransceiver assembly. In response to a user input, the remote server maybe configured to generate a second display, in the first user interface,from at least a portion of the more detailed data received from thetransceiver assembly and corresponding to a time associated with thedata displayed in the first display.

The remote server may be further configured, in response to the userinput, to send the signal to the transceiver assembly.

Another embodiment of the present invention provides a multi-tieredmethod for remote monitoring of data. According to the method, data isreceived. The received data is stored in a memory. The stored data isreferred to as “more detailed data.” A subset of the received data(referred to as “less detailed data”) is wirelessly sent to a remoteserver. The less detailed data sent to the remote server ischaracterized by: a lower resolution than the more detailed data storedin the memory for a corresponding time period and/or a lower samplingrate than the more detailed data stored in the memory for acorresponding time period and/or having been received from a differentset of the sensors than the more detailed data stored in the memory fora corresponding time period. Responsive to a signal from the remoteserver, at least a portion of the more detailed data is fetched from thememory. The fetched more detailed data is sent to the remote server.

The less detailed data sent to the remote server may be characterizedby: a lower resolution than the more detailed data stored in the memoryfor a corresponding time period and/or a lower sampling rate than themore detailed data stored in the memory for a corresponding time period.

In addition, the less detailed data may be received at the remoteserver. The received less detailed data may be automatically analyzed,at the remote server, for an indication of an anomaly. If the anomaly isindicated, the signal may be automatically sent.

The more detailed data may be received by a remote server, and thereceived more detailed data may be automatically analyzed, at the remoteserver, to verify the indicated anomaly.

Analyzing the less detailed data may include analyzing the less detaileddata according to a first analytic technique. Analyzing the moredetailed data may include analyzing the more detailed data according toa second analytic technique. The second analytic technique may have ahigher specificity for the anomaly than the first analytic technique.

Yet another embodiment of the present invention provides a multi-tiereddata collection system for use with a remote server. The system includesa digital data input source and a transmitter assembly. The transmitterassembly includes a memory, a controller and a wireless transmitter. Thetransmitter assembly is communicatively coupled to the digital datainput source. The transmitter assembly is configured to receive datafrom the digital data input source and store the received data in thememory. The stored data is referred to as “more detailed data.” Thetransmitter assembly is also configured to automatically analyze asubset of the received data (referred to as “less detailed data”) for anindication of an anomaly. The less detailed data is characterized by: alower resolution than the more detailed data stored in the memory for acorresponding time period and/or a lower sampling rate than the moredetailed data stored in the memory for a corresponding time periodand/or having been received from a different set of the sensors than themore detailed data stored in the memory for a corresponding time period.If the anomaly is indicated, the transmitter assembly is configured toautomatically fetch at least a portion of the more detailed data fromthe memory and send the fetched more detailed data to the remote server.

The less detailed data may be characterized by at least one of: a lowerresolution than the more detailed data stored in the memory for acorresponding time period and/or a lower sampling rate than the moredetailed data stored in the memory for a corresponding time period.

The remote server may be configured to receive the more detailed dataand automatically analyze the received more detailed data to verify theindicated anomaly.

The transmitter assembly may be configured to analyze the less detaileddata according to a first analytic technique, and the remote server maybe configured to analyze the more detailed data according to a secondanalytic technique. The second analytic technique may have a higherspecificity for the anomaly than the first analytic technique.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be more fully understood by referring to thefollowing Detailed Description of Specific Embodiments in conjunctionwith the Drawings, of which:

FIG. 1 is a schematic block diagram of an embodiment of the presentinvention.

FIG. 2 is a more detailed schematic block diagram of an embodiment ofthe present invention.

FIG. 3 is a schematic diagram illustrating one possible combination ofphysiological sensors and a possible placement of the sensors on a torsoof a patient, according to an embodiment of the present invention.

FIG. 4 contains a hypothetical ECG waveform representing detailed datacollected from the sensors of FIG. 3 and stored in a memory, accordingto an embodiment of the present invention.

FIG. 5 contains a waveform representing a less detailed version of thedata collected from the sensors of FIG. 3 and sent to a remote server,according to an embodiment of the present invention.

FIG. 6 contains a waveform representing the more detailed data atransceiver assembly sends to the remote server in response to a requestfrom the server, according to an embodiment of the present invention.

FIG. 7 contains a table of exemplary resolutions, sample rates andtransmission duty cycles, according to an embodiment of the presentinvention.

FIG. 8 contains a table that lists exemplary threshold values forseveral patient activity levels, according to an embodiment of thepresent invention.

FIG. 9 is a flowchart illustrating a process for calculating arespiration rate, according to an embodiment of the present invention.

FIG. 10 is a schematic block diagram of an embodiment of the presentinvention.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

In accordance with embodiments of the present invention, methods andapparatus are disclosed for locally collecting and locally storing datafrom a monitored item, such as an ambulatory patient, wirelessly sendingonly a proper subset of the collected data to a remote central serverand there automatically analyzing the sent data in real time. The sentsubset of the collected data is less detailed than the data collectedand stored by a local data collector.

The central server employs a two-tiered analysis methodology. If thefirst tier, which performs a high-sensitivity but low-specificityanalysis, detects a possible anomaly, such as an arrhythmia, in thereceived subset of the collected data, the server requests the datacollector to retrospectively send more detailed data the collectorpreviously stored, i.e., more detailed data from around the time of thesuspected anomaly.

The second tier performs a high-specificity analysis of the moredetailed data to confirm or refute (“verify”) the suspected anomaly.Thus, overall utilization of the wireless channel used to send the datais kept low by sending detailed data only when necessary to verify asuspected anomaly. Furthermore, electrical power (battery) andcomputational resource requirements of the data collector are kept low,because the data collector performs no data analysis.

Thus, significantly, embodiments of the present invention enable theremote server to operate primarily on a less detailed subset ofcollected data and retrospectively obtain more detailed data whennecessary to verify a suspected anomaly. In contrast, no known prior artdata monitor stores detailed collected data locally and sends only asubset of the collected data to a remote server. No known prior artremote server requests more detailed data from an earlier time period(“retrospectively requests data”) in response to detecting a suspectedanomaly and then uses the more detailed data to verify the suspectedanomaly.

A “subset” of the collected data means less than all of the collecteddata. The subset may, for example, be a downsampled (lower samplingrate) or quantized (less accurate samples) version of the collecteddata. The subset may include data from one or more sensors or one ormore types of data, such as seismic activity, seismic tilt, temperature,pressure, wind speed, wind direction, water temperature, flow rate, waveheight, heart rate, ECG waveform, respiration rate, SpO2, bloodpressure, body movement (such as provided by accelerometers). The moredetailed data may include data from all the same, some of the same ordifferent sensors or different types of data. SpO2 is a measure of theamount of oxygen attached to hemoglobin cells in circulating bloodsystem. SpO2 is typically given as a percentage, normal is around 96%.The “S” in SpO2 stands for “saturation.”

FIG. 1 is a schematic block diagram of an embodiment of the presentinvention. The disclosed embodiments relate to remote monitoring ofambulatory patients and, more specifically, to remote monitoring anddetection of arrhythmias or other health-related issues. However,principles disclosed herein are applicable to many other areas, such asoil and gas exploration, weather forecasting, earthquake or tornadoearly warning, etc. The principles disclosed herein are advantageouslyapplicable in situations where detection of an anomalous data orbehavior (collectively an “anomaly”) is desirable, but remotelycollecting sufficient data to accurately detect the anomaly with bothhigh sensitivity (few false negatives) and high selectivity (few falsepositives) is difficult. In FIG. 1, a data collector and set ofphysiologic sensors (collectively identified at 100) is assigned to eachmonitored patient. The physiologic sensors are attached to the patient,and data collected from the sensors are stored in a memory 103 withinthe data collector 100. Time stamps, corresponding to times at which thedata were collected, or other suitable data timing information is alsostored in the memory 103. If the memory 103 becomes full or reaches apredetermined fullness, the data collector 100 begins overwritingpreviously stored data, beginning with the oldest data. Thus, the memory103 stores the most recently collected data on a rolling basis.

The data collector 100 includes, or is coupled to, a suitable wirelesstransceiver 104, such as a cellular telephone. A subset of the collecteddata (identified as “less detailed data” 106), including informationabout when the data were collected, is sent wirelessly to a centralremote server 107, such as via a cellular telephone network 108. Theless detailed data 106 may be a downsampled version of the collecteddata. That is, the less detailed data 106 may have a lower sampling ratethan the collected and stored data. For example, only every Nth sampleof the collected data may be included in the less detailed data 106,where N is an integer or rational fraction that provides a sampling ratesufficient for the first tier analysis. Optionally or alternatively, theless detailed data 106 may be a quantized version of the collected data.That is, the less detailed data 106 may be rounded or otherwise includefewer digits of accuracy than the collected data, although sufficientfor the first tier analysis.

The central server 107 may serve many per-patient data collectors 100.The central server 107 performs a high-sensitivity analysis 109 of theless detailed data 106. The high-sensitivity analysis 109 is configuredsuch that it has a low probability of generating a false negativeresult. That is, the high-sensitivity analysis 109 is not likely to failto detect an actual arrhythmia. However, to achieve this high level ofsensitivity, the high-sensitivity analysis 109 is likely to generate arelatively large number of false positive results, i.e., the firstanalytical tier may have low specificity.

A relatively large number of false positive results is, however,acceptable for several reasons, including only a relatively small subsetof the collected physiological data is sent via the wireless channel108, thereby conserving the wireless channel's carrying capacity.Conserving wireless channel carrying capacity may be important tosupport a large number of per-patient data collectors 100 over thewireless channel 108 and/or to enable the wireless channel 108 to carryother types of traffic, such as text messages, streaming video and voicetelephone calls, most or all of which may be unrelated to thephysiological monitoring described here. Thus, at least conceptually,false positives are traded, at least in part, for increased wirelesschannel capacity. Furthermore, the bulk or all of the false positivesare filtered out by the second tier of analysis, as described next.

If the high-sensitivity analysis 109 detects a suspected arrhythmia, thehigh-sensitivity analysis 109 sends a request 112 to the data collector100. The request 112 identifies a time period of interest, such as atime period surrounding the time at which the data that lead to thesuspicion were collected. In response to the request 112, the datacollector 100 fetches more detailed data for the requested time periodfrom the memory 103 and sends the more detailed data 115 to the centralserver 107, and then a high-specificity analysis 118 is performed on themore detailed data 115. Preferably, the second analytical tier 118 isalso high in sensitivity, so it has a low probability of generating afalse negative result.

The high-specificity analysis 118 is configured such that it has a lowprobability of generating false positive results. That is, thehigh-specificity analysis 118 is not likely to indicate an arrhythmiawhen none actually occurred. If the high-specificity analysis 118verifies that an arrhythmia occurred, an alarm may be raised orinformation may be displayed 121, such as to alert a physician ortechnician.

In order to provide results with high specificity and high sensitivity,the high-specificity analysis 118 needs the more detailed data 115, aswell as typically consuming more computational resources than thehigh-sensitivity analysis 109. Requesting 112 and sending 115 the moredetailed data utilizes a portion of the wireless channel capacity.However, this utilization occurs only relatively infrequently, i.e.,when the high-sensitivity analysis 109 detects a suspected arrhythmia.In addition, the high-specificity analysis 118 consumes a relativelylarge amount of computational resources. Again, however, thisconsumption occurs only relatively infrequently.

Thus, the two-tiered analysis 109 and 118 can be seen, at leastconceptually, as a tradeoff between, on one hand, complexity involvingtwo separate analysis tiers and occasional high wireless channel andcomputation resource utilization and, on the other hand, an overallreduction of wireless channel and computational resource utilization.The overall scheme disclosed herein requires fewer computationalresources, and correspondingly less power (battery), on the per-patientdata collector 100 than prior art schemes that attempt to analyze thecollected data at the per-patient device and notify a central systemonly when an arrhythmia is detected. In addition, the overall schemeuses less wireless channel capacity and fewer central analysis serverresources than prior art systems that send constant streams of allcollected data to a central server for analysis.

Furthermore, the overall scheme is well suited for implementation in a“cloud computing” environment, where computing resources are availableon demand. Thus, in some embodiments, the additional computationalresources required for the high-specificity analysis 118 need not bepre-allocated and, therefore, idle most of the time. Instead,computational resources for the high-specificity analysis 118 can bedynamically and automatically utilized, requested or scheduled wheneverthey are required. Such a cloud computing environment is available fromAmazon Web Services LLC under the trade name Amazon Elastic ComputeCloud (Amazon EC2) and RightScale cloud management from RightScale, Inc.

FIG. 2 is a schematic block diagram of an embodiment of the presentinvention, showing more detail than FIG. 1. One or more physiologicalsensors 200 are coupled to a transceiver assembly 203. The coupling maybe provided by via a short-range wireless system, such as Bluetoothtransceivers. Alternatively, the coupling may be provided by wires oroptical cable. The transceiver assembly 203 includes a memory 103 and along-range wireless transceiver 104, such as a cellular telephonetransceiver, as discussed above. The long-range wireless transceiver 104may be replaced by any suitable wireless transceiver, such as a WiFitransceiver (not shown).

A controller 206 directs operation of the transceiver assembly 203. Thecontroller 206 may be implemented by a microprocessor executinginstructions stored in a memory, such as the memory 103 or anothermemory. The controller 206 receives data from the sensors 200 and storesthe received data in the memory 103. The controller 206 also provides aless detailed version 106 of the sensor data to the long-range wirelesstransceiver 104 for transmission, via the wireless network 108, to theremote server 107. The controller 206 may be coupled to the long-rangewireless transceiver 104 via wires, optical cables or a short-rangewireless system, such as Bluetooth.

Optionally or alternatively, part or all of the functions of thecontroller 206 and the memory 103 may be implemented by a processor anda memory within the long-range wireless transceiver 104. For example, a“smart phone” may store and execute an application program (software)207 configured to receive the data from the sensors 200, store thereceived sensor data in a memory of the smart phone and transmit asubset of the collected data to the remote server 107. In response tothe request 112 from the remote server 107, the application program 207may fetch the more detailed data 115 and send it to the remote server107. Furthermore, the application program 207 may alter, such as inresponse to commands from the remote server 107, data collectionparameters, such as sampling rate and sampling precision, and datatransmission parameters, such as sampling rate and sampling precision ofthe less detailed data 106 and of the more detailed data 115, as well astransmission packet size, packet transmission rate, number of samplesper packet, etc.

The controller 206 and the long-range wireless transceiver 104 shouldcheck authenticity of each other and authority to receive data and to becontrolled by each other, prior to engaging in substantivecommunications, transmission of sensor data, control, etc. Furthermore,data and control communications, particularly wireless communications,between and among components of embodiments should be encrypted. Forexample, wireless data communications between the sensors 200 and thecontroller 206, between the controller 206 and the long-range wirelesstransceiver 104 and between the long-range wireless transceiver 104 andthe remote server 107 should be suitably encrypted, such as to protectpatient privacy.

The transceiver assembly 203 may be implemented as one physicalassembly. Alternatively, the transceiver assembly 203 may be implementedas two physically separable components, one component including thecontroller 206 and the memory 103, and the other component including thelong-range wireless transceiver 104. Such a two-part division isindicated by dashed line 208. The two components may communicate witheach other via a short-range wireless system, such as Bluetooth (notshown). The tasks of receiving the data from the sensors 200, storingthe received data in the memory 103 or in a memory in a smart phone andgenerating the less detailed data 106 from the collected data may bedivided or allocated between the controller 206 and the smart phone.

A suitable gateway 209, as well as other well-known computer networkingequipment, such as network switches, routers, firewalls and the like,may be used to couple the remote server 107 to the wireless network 108.The remote server 107 includes a physiological data analyzer 212, whichis configured to perform the high-sensitivity analysis 109 and thehigh-specificity analysis 118 discussed above, with respect to FIG. 1.The remote server 107 may include a database 215, and the data analyzer212 may be configured to store the received less detailed data 106and/or the received more detailed data 115, or a portion thereof, in thedatabase 215. The data may be stored in the database 215 in an encryptedform to increase security of the data against unauthorized access.

A physician application program 218 allows a physician to controlparameters of the system, such as threshold values used by the dataanalyzer 212 in performing the high-sensitivity 109 and/or thehigh-specificity 118 analyses. Optionally, the physician applicationprogram 218 also allows the physician to set operating parameters of thetransceiver assembly 203, such as the amount by which the less detaileddata is downsampled, quantized, etc.

The physician application program 218 also displays data to thephysician and allows the physician to select types of data to display,time periods of the data to display, levels of data detail to displayand other operating parameters of the system. For example, the physicianmay select a beginning and ending time surrounding a suspected orverified arrhythmia for display. In response to a query by thephysician, the physician application program 218 may fetch and displaydata from the database 215. If the requested data is not available inthe database 215, or if the requested data is not available in thedatabase 215 at the level of detail requested by the physician, thephysician application program 218 may automatically communicate with thetransceiver assembly 203 to fetch the appropriate data in theappropriate amount of detail.

The physician application program 218 may implement appropriate securityprotocols, such as requiring the physician to enter logon credentials,so as to appropriately limit access to patient data and comply withregulations, such as the Health Insurance Portability and AccountabilityAct (HIPAA).

A user interface/web server 221 accepts user (physician, patient oradministrator) inputs and generates appropriate displays to facilitateuser interaction with the physician application program 218 and asimilar patient application program 214, described below. The userinterface/web server 221 may generate a window-metaphor based computeruser interface on a screen (not shown) coupled to the remote server 107,or the user interface/web server 218 may generate web pages that arerendered by a browser 227 executed by a separate user computer (notshown). The web server 221 and the web browser 227 may communicate witheach other using an appropriate encrypted protocol, such as HypertextTransfer Protocol Secure (HTTPS).

The patient application program 224 provides access by a patient to herown data, using appropriate patient logon credentials and anappropriately secured browser connection.

FIG. 3 is a schematic diagram illustrating one possible combination ofphysiological sensors 300, 303 and 309 and a possible placement of thesensors on a torso 312 of a patient. One of the sensors 309 may beattached at about the elevation of the diaphragm of the patient. Eachsensor 300-309 may be attached to the torso 312 using well-known gelpads or other conventional attachment techniques. Any combination ofwell-known physiological electrodes may be used for the sensors 300-309.For example, the sensors 300-309 may include any combination of SpO2sensors, blood pressure sensors, heart electrodes, respiration sensors,movement and activity sensors, and the like. Movement or activity may besensed with appropriate accelerometers or gyroscopes, such as microelectro-mechanical system (MEMS) devices. The sensors 300-309 may beconnected via wires or optical cables 315 and 318 or via wireless links,such as Bluetooth links Respiration data may be derived from ECGbaseline data, as is known to those of skill in the art.

The transceiver assembly 203 (FIG. 2), or a portion thereof, may beattached to, and supported by, one of the sensors 309, as indicated at321. Optionally, other sensors, such as a patient weight measuringdevice, blood pressure cuff, etc., may be disconnectably coupled viawires, optical cables or wirelessly to the transceiver assembly 203.

As noted, the transceiver assembly 203 collects physiologic data, storesthe collected data in a memory 103 and sends a less detailed version ofthe data 106 to the remote server 107. Upon detecting a suspectedarrhythmia, the remote server 107 requests 112 more detailed data. FIG.4 contains a hypothetical ECG waveform 400, representing detailed datacollected from the sensors 200 and stored in the memory 103. That is,the collected data has a relatively high sampling rate and a relativelyhigh resolution. Assume the waveform 400 includes a portion 403, duringwhich the waveform is anomalous.

FIG. 5 contains a waveform 500, representing a less detailed version 106of the collected data. The less detailed data 106 is transmitted to theremote server 107. The high-sensitivity analysis 109 (FIG. 1) performedby the data analyzer 212 (FIG. 2) detects the anomaly 403 as a suspectedarrhythmia. Responsive to this detection, the data analyzer 212 (FIG. 2)sends a request 112 to the transceiver assembly 203 for more detaileddata for a time period 503 around the anomaly 403. The length of theperiod 503 may depend on the type of anomaly detected by the dataanalyzer 212. Various types of anomalies, and corresponding time periods503, may be specified by the physician via the physician applicationprogram 218.

FIG. 6 contains a waveform 600, representing the more detailed data 115(FIG. 2) the transceiver assembly 203 sends to the remote server 107.The more detailed data 115 has a higher sampling rate, higher resolutionand/or contains data from more sensors than the less detailed data 106.Using the more detailed data 115, the high-specificity analysisperformed by the data analyzer 212 verifies the suspected arrhythmia603.

FIG. 7 contains a table 700 of exemplary resolutions, sample rates andtransmission duty cycles (times between data transmissions from thetransceiver assembly 203 to the remote server 107). Each row of thetable 700 represents a different combination of these parameters. Eachrow is labeled with a “Setting,” indicating relative timeliness of thedata feed from the transceiver assembly 203, such as based on relativeseriousness of the patient's condition. Thus, the transceiver assembly203 may store more highly resolved data (in terms of the number of bitsper sample), more data samples (in terms of the number of samples persecond) and/or data from more sensors or more types of sensors than aresent to the remote server 107. Furthermore, the transceiver assembly 203may store data for a period of time after data representing that timeperiod has been sent to the remote server 107. The specific settings inthe table 700 are only examples of what a physician may determine from arange of possible values.

The remote server 107 may be configured to determine data collectionparameters, either manually, such as in response to inputs received viathe physician application program 218, or automatically, such as inresponse to collected data meeting one or more predetermined criteria,such as detecting an anomaly in the collected data. A physician mayselect, via the physician application program 218, one of the sets ofdata collection parameters shown in table 700, or the physician mayspecify custom values, such as values for each patient, by entering thevalues via the physician application program 218. The physician mayspecify, via the physician application program 218, different datacollection parameters for different time periods of a day, differentdays or any other specified time periods. Similarly, through thephysician application program 218, the physician may alter thresholdvalues, against which the data analyzer 212 compares collected data.Optionally or alternatively, which set of data collection parameters,i.e., which row of the table 700, is used may depend in part or in wholeon the amount of charge remaining in the battery that powers thetransceiver assembly 203, the sensors 200 (if there is a separatebattery for the sensors) and/or the long-range wireless transceiver 104.Less remaining charge may cause selection of a lower setting in thetable 700.

In some embodiments, data collection and/or transmission parameters maybe automatically changed in response to automatically detecting ameasured physiologic data value exceeding or falling below apredetermined threshold. For example, if respiration rate, SpO2 or bloodpressure exceeds a high-limit threshold or falls below a low-limitthreshold, the remote server 107 can instruct the transceiver assembly203 to increase the rate at which data is sampled from the sensors 200and/or transmitted as less detailed data 106 or more detailed data 115to the remote server 107. Similarly, the data sampling resolution anddata transmission rate (from the transceiver assembly 203) or otherparameter (collectively referred to herein as “data collectionparameters”) may be increased.

Some or all of the thresholds may be predetermined or they may bespecified on a per-patient basis by the physician via the physicianapplication program 218. Optionally or alternatively, some or all of thethresholds may be automatically determined based on collected data. Forexample, if data collected from a patient indicates to the remote server107 that the patient is exercising, i.e., if for example data from theaccelerometers indicates body movements consistent with the patientperforming jumping jacks or sit-ups, thresholds for respiration andheart rate may be automatically increased until after these movementscease to be detected, plus an optional rest period. FIG. 8 contains atable 800 that lists exemplary threshold values for several patientactivity levels.

Optionally, after the metric that caused a data collection parameter tobe increased returns to normal for at least a predetermined period oftime, the data collection parameter may be returned to its originalvalue or a value intermediate the increased value and its originalvalue. The data collection parameter may be returned to its originalvalue in timed stages or stages based on measured data values.

The anomaly that triggers request 112 for retrospective data or a changein the data collection parameters may be more complex than a measuredvalue exceeding or falling below a threshold value. In some embodiments,an automatically detected anomaly in the measured ECG automaticallytriggers the request 112 for retrospective data or altering one or moredata collection parameters. For example, the ECG data may be processedby the data analyzer 212 to automatically classify heartbeats usingmorphology and heartbeat interval features, as described by Philip deChazal, et al., in “Automatic Classification of Heartbeats Using ECGMorphology and Heartbeat Interval Features,” IEEE Transactions onBiomedical Engineering, Vol. 51, No. 7, July, 2004, the contents ofwhich are hereby incorporated by reference. In other words, collecteddata may be processed, before a determination is made whether an anomalyhas been detected.

As noted, arrhythmia may be suspected or verified (or both) using ECGdata, non-ECG data or a combination thereof. For example, an arrhythmiamay be suspected or verified, based in whole or in part on respirationrate. The respiration rate may be determined based on data from one ormore accelerometers in the sensors attached to the torso of the patient,as shown for example in FIG. 3. Chest movements detected by theaccelerometers may be filtered, such as within expected frequencies andamplitudes, to derive the respiration rate. For example, oneaccelerometer may be included in in the sensor 309 (FIG. 3), which islocated adjacent the patient's diaphragm, and another accelerometer maybe include in the sensor 300 or 303. Relative motion between the twolocations on the torso 312 represented by the two accelerometers closelyrepresents diaphragm movement and, therefore, breathing.

The respiration rate may also, or alternatively, be derived from ECGbaseline data, as is well known in the art. Either of these respirationrates may be used by the data analyzer 212. However, some embodimentsuse both derived rates, as shown in a flowchart in FIG. 9. At 900, ECGand accelerometer data are collected. At 903, a first candidaterespiration rate is calculated, based on the ECG baseline data, and at906, a second candidate respiration rate is calculated, based on theaccelerometer data. These two candidate rates are compared at 909. Ifthe difference between the two candidate rates is less than apredetermined value, such as about 10%, an average of the two candidaterates is calculated at 912, and this average is used 915 by the dataanalyzer 212. Optionally, the maximum allowable difference between thetwo candidate rates, i.e., the limit in 909, may be specified by thephysician via the physician application program 218.

However, if the two candidate rates differ by more than thepredetermined value, control passes to 918. If both candidate rates areoutside a predetermined range of normal respiration rates, bothcandidate rates are discarded 921, and the procedure loops back to 900.If both candidate rates are not outside the predetermined range ofnormal respiration rates, i.e., if at least one of the candidate ratesis within the range, control passes to 924.

At 924, if both candidate rates are within the predetermined normalrange, the ECG-based candidate respiration rate is used at 927. However,if only one of the candidate rates is within the predetermined normalrange, control passes to 930.

At 930, if only the ECG-based candidate respiration rate is within thepredetermined normal range, the ECG-based candidate respiration rate isused at 933. However, at 930, if the ECG-based candidate respirationrate is not within the predetermined normal range, theaccelerometer-based candidate respiration rate is used at 936.

Although embodiments in which all the data analysis is performed by theremote server 107 (FIG. 2) have been described, the high-sensitivityanalysis 109 (FIG. 1) may optionally or alternatively be performed bythe controller 206 or the cellular transceiver 104, i.e., at thepatient, rather than in the remote server 107, as schematicallyillustrated in FIG. 10. In this case, if an arrhythmia is suspected bythe high-sensitivity analysis 1000, no request signal needs to be sentto the per-patient physiologic sensors and data collector 1003. Instead,the controller 206 (see FIG. 2) or the cellular transceiver 104 (seeFIG. 2) automatically sends the more detailed data to the remote server1006, and the remote server 1006 performs the high-specificity analysis118, as described above. In such an embodiment, the transceiver assembly203 (see FIG. 2) may be referred to as a transmitter assembly, becauseit primarily or exclusively sends data to the remote server 1006 anddoes not necessarily receive any requests 112 (See FIGS. 1 and 2) fromthe remote server 1006.

Although embodiments of the present invention have been described asdetecting and verifying suspected arrhythmias, other embodiments may besimilarly configured and used to detect and verify other health orfitness conditions, such as inappropriate insulin level, respiration,blood pressure, SpO2, body movement, exertion and the like. Furthermore,other embodiments may be configured and used to detect and verifynon-health and non-fitness related conditions, such as unsafe pressureor flow rate in oil or gas wells, unsafe level of combustible gas in amine, tornado, heavy rain or other severe weather, earthquake, mudslide,tsunami, excessive stress or movement within a geological feature (suchas a fault), explosion, decompression, suspension bridge tension orsway, excessive stress within a dam, excessive stress or movement of awing or other structural member of an aircraft or spacecraft, failure ofa structural member, nuclear radiation leakage and the like. A man-madeitem that includes at least one structural member is referred to hereinas a “construct.” Exemplary constructs include wells, bridges, dams,buildings (such as office buildings, dwellings, theaters and the like),statues, aircraft and spacecraft. In addition, although a two-tieredsystem has been described, the principles described herein may beapplied to systems that employ three or more tiers of analysis. In sucha system, the analysis performed at each tier is typically more specificthan the analysis performed in its preceding tier, and each tier (otherthan the last tier) triggers a request from a remote data monitor tosend data that is more detailed than the data sent to the requestingtier. Each tier's analysis may consume more computational resources thanthe immediately previous tier's analysis.

A remote health or other monitoring system includes a processorcontrolled by instructions stored in a memory. For example, thetransceiver assembly may include and be controlled by such a processor,and the remote server may be controlled by another such processor. Thememory may be random access memory (RAM), read-only memory (ROM), flashmemory or any other memory, or combination thereof, suitable for storingcontrol software or other instructions and data.

Some of the functions performed by the remote health or other monitoringsystem have been described with reference to flowcharts and/or blockdiagrams. Those skilled in the art should readily appreciate thatfunctions, operations, decisions, etc. of all or a portion of eachblock, or a combination of blocks, of the flowcharts or block diagramsmay be implemented as computer program instructions, software, hardware,firmware or combinations thereof

Those skilled in the art should also readily appreciate thatinstructions or programs defining the functions of the present inventionmay be delivered to a processor in many forms, including, but notlimited to, information permanently stored on non-writable storage media(e.g. read-only memory devices within a computer, such as ROM, ordevices readable by a computer I/O attachment, such as CD-ROM or DVDdisks), information alterably stored on writable storage media (e.g.floppy disks, removable flash memory and hard drives) or informationconveyed to a computer through communication media, including wired orwireless computer networks.

In addition, while the invention may be embodied in software, thefunctions necessary to implement the invention may optionally oralternatively be embodied in part or in whole using firmware and/orhardware components, such as combinatorial logic, Application SpecificIntegrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs) orother hardware or some combination of hardware, software and/or firmwarecomponents.

The embodiments of the invention described above are intended to bemerely exemplary. While the invention is described through theabove-described exemplary embodiments, it will be understood by those ofordinary skill in the art that modifications to, and variations of, theillustrated embodiments may be made without departing from the inventiveconcepts disclosed herein. For example, although some aspects of remotehealth monitoring system have been described with reference to aflowchart, those skilled in the art should readily appreciate thatfunctions, operations, decisions, etc. of all or a portion of eachblock, or a combination of blocks, of the flowchart may be combined,separated into separate operations or performed in other orders.Furthermore, disclosed aspects, or portions of these aspects, may becombined in ways not listed above. Accordingly, the invention should notbe viewed as being limited to the disclosed embodiments.

1. A multi-tiered data collection system for use with a remote server,the system comprising: a digital data input source; a transceiverassembly comprising a memory, a controller and a wireless transceiver,the transceiver assembly being communicatively coupled to the digitaldata input source and configured to: receive data from the digital datainput source; store the received data in the memory (“more detaileddata”); send a subset of the received data (“less detailed data”), viathe wireless transceiver, to the remote server, wherein the lessdetailed data sent to the remote server is characterized by at least oneof: a lower resolution than the more detailed data stored in the memoryfor a corresponding time period, a lower sampling rate than the moredetailed data stored in the memory for a corresponding time period andhaving been received from a different set of the sensors than the moredetailed data stored in the memory for a corresponding time period; andresponsive to a signal from the remote server, fetch at least a portionof the more detailed data from the memory and send the fetched moredetailed data to the remote server.
 2. A system according to claim 1,wherein the less detailed data sent to the remote server ischaracterized by at least one of: a lower resolution than the moredetailed data stored in the memory for a corresponding time period and alower sampling rate than the more detailed data stored in the memory fora corresponding time period.
 3. A system according to claim 1, whereinthe remote server is configured to: receive the less detailed data sentby the transceiver assembly; automatically analyze the received lessdetailed data for an indication of an anomaly; and if the anomaly isindicated, automatically send the signal to the transceiver assembly. 4.A system according to claim 3, wherein the anomaly comprises anearthquake.
 5. A system according to claim 3, wherein the anomalycomprises a tsunami.
 6. A system according to claim 3, wherein theanomaly comprises an unsafe condition within at least one of a gas well,an oil well and a mine.
 7. A system according to claim 3, wherein theanomaly comprises severe weather.
 8. A system according to claim 3,wherein the anomaly comprises an unsafe mechanical condition in astructural member of a construct.
 9. A system according to claim 3,wherein the anomaly comprises failure of a structural member of aconstruct.
 10. A system according to claim 3, wherein the anomalycomprises an unsafe condition within a geological structure.
 11. Asystem according to claim 3, wherein the anomaly comprises a nuclearradiation level that exceeds a predetermined value.
 12. A systemaccording to claim 3, wherein the anomaly comprises an explosion.
 13. Asystem according to claim 3, wherein the anomaly comprises adecompression.
 14. A system according to claim 3, wherein the remoteserver is further configured to: receive the more detailed data; andautomatically analyze the received more detailed data to verify theindicated anomaly.
 15. A system according to claim 14, wherein theremote server is configured to: analyze the less detailed data accordingto a first analytic technique; and analyze the more detailed dataaccording to a second analytic technique; wherein the second analytictechnique has a higher specificity for the anomaly than the firstanalytic technique.
 16. A system according to claim 14, wherein theremote server is configured to: display a first user interfaceconfigured to accept at least one user-specified criterion; andautomatically analyze the received less detailed data for the indicationof the anomaly, based on at least a portion of the less detailed datameeting the user-specified criterion.
 17. A system according to claim14, wherein the remote server is configured to: display a first userinterface configured to accept at least one user-specified criterion;and automatically analyze the received more detailed data to verify theindicated anomaly, based on at least a portion of the more detailed datameeting the user-specified criterion.
 18. A system according to claim 1,wherein the wireless transceiver comprises a cellular telephone.
 19. Asystem according to claim 1, wherein the wireless transceiver assemblycomprises a cellular telephone coupled via a short-range wireless linkto the wireless transceiver and configured to store the more detaileddata in the memory, send the less detailed data to the remote server,responsive to the signal fetch the at least the portion of the moredetailed data from the memory and send the fetched more detailed data tothe remote server via a wireless carrier network.
 20. A system accordingto claim 1, further comprising a cellular telephone configured to becommunicatively coupled to a wireless carrier network and to: receivethe data sent by the transceiver assembly via the wireless transceiver;and send the received data via the wireless carrier network to theremote server.
 21. A system according to claim 1, further comprising anapplication program configured to be executed by a cellular telephoneconfigured to be communicatively coupled to a wireless carrier network,the application program being configured to: receive the data sent bythe transceiver assembly via the wireless transceiver; and send thereceived data via the wireless carrier network to the remote server. 22.A system according to claim 1, wherein: the remote server is configuredto: accept, through a first user interface, a user-specified datacollection parameter; and in response to accepting the user-specifieddata collection parameter, send the data collection parameter to thetransceiver assembly; and the transceiver assembly is configured to:receive the data collection parameter; and in response to receipt of thedata collection parameter, change at least one of the resolution andsampling rate of the less detailed data thereafter sent to the remoteserver.
 23. A system according to claim 1, wherein the remote server isconfigured to: generate a fist display, in a first user interface, fromthe less detailed data received from the transceiver assembly; inresponse to a user input, generate a second display, in the first userinterface, from at least a portion of the more detailed data receivedfrom the transceiver assembly and corresponding to a time associatedwith the data displayed in the first display.
 24. A system according toclaim 23, wherein the remote server is further configured, in responseto the user input, to send the signal to the transceiver assembly.
 25. Amulti-tiered method for remote monitoring of data, the methodcomprising: receiving data; storing the received data in a memory (“moredetailed data”); wirelessly sending a subset of the received data (“lessdetailed data”) to a remote server, wherein the less detailed data sentto the remote server is characterized by at least one of: a lowerresolution than the more detailed data stored in the memory for acorresponding time period, a lower sampling rate than the more detaileddata stored in the memory for a corresponding time period and havingbeen received from a different set of the sensors than the more detaileddata stored in the memory for a corresponding time period; andresponsive to a signal from the remote server, fetching at least aportion of the more detailed data from the memory and sending thefetched more detailed data to the remote server.
 26. A method accordingto claim 25, wherein the less detailed data sent to the remote server ischaracterized by at least one of: a lower resolution than the moredetailed data stored in the memory for a corresponding time period and alower sampling rate than the more detailed data stored in the memory fora corresponding time period.
 27. A method according to claim 25, furthercomprising: receiving, at the remote server, the less detailed data;automatically analyzing, at the remote server, the received lessdetailed data for an indication of an anomaly; and if the anomaly isindicated, automatically sending the signal.
 28. A method according toclaim 27, further comprising: receiving, by the remote server, the moredetailed data; and automatically analyzing, at the remote server, thereceived more detailed data to verify the indicated anomaly.
 29. Amethod according to claim 28, wherein: analyzing the less detailed datacomprises analyzing the less detailed data according to a first analytictechnique; analyzing the more detailed data comprises analyzing the moredetailed data according to a second analytic technique; and the secondanalytic technique has a higher specificity for the anomaly than thefirst analytic technique.
 30. A multi-tiered data collection system foruse with a remote server, the system comprising: a digital data inputsource; a transmitter assembly comprising a memory, a controller and awireless transmitter, the transmitter assembly being communicativelycoupled to the digital data input source and configured to: receive datafrom the digital input source; store the received data in the memory(“more detailed data”); automatically analyze a subset of the receiveddata (“less detailed data”) for an indication of an anomaly, wherein theless detailed data is characterized by at least one of: a lowerresolution than the more detailed data stored in the memory for acorresponding time period, a lower sampling rate than the more detaileddata stored in the memory for a corresponding time period and havingbeen received from a different set of the sensors than the more detaileddata stored in the memory for a corresponding time period; and if theanomaly is indicated, automatically fetch at least a portion of the moredetailed data from the memory and send the fetched more detailed data tothe remote server.
 31. A system according to claim 30, wherein the lessdetailed data is characterized by at least one of: a lower resolutionthan the more detailed data stored in the memory for a correspondingtime period and a lower sampling rate than the more detailed data storedin the memory for a corresponding time period.
 32. A system according toclaim 30, wherein the remote server is configured to: receive the moredetailed data; and automatically analyze the received more detailed datato verify the indicated anomaly.
 33. A system according to claim 32,wherein: the transmitter assembly is configured to analyze the lessdetailed data according to a first analytic technique; and the remoteserver is configured to analyze the more detailed data according to asecond analytic technique; wherein the second analytic technique has ahigher specificity for the anomaly than the first analytic technique.